{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e1c59071",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/output_parsing/GuardrailsDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c48213d-6e6a-4c10-838a-2a7c710c3a05",
   "metadata": {},
   "source": [
    "# Guardrails Output Parsing\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc25e9e7",
   "metadata": {},
   "source": [
    "First, set your openai api keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f08573d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import os\n",
    "\n",
    "# os.environ[\"OPENAI_API_KEY\"] = \"sk-...\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cf54a5a8",
   "metadata": {},
   "source": [
    "If you're opening this Notebook on colab, you will probably need to install LlamaIndex 🦙."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9c58d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n",
      "Requirement already satisfied: llama-index-output-parsers-guardrails in /Users/zaydsimjee/workspace/zayd_forks/llama_index/.venv/lib/python3.11/site-packages (0.1.2)\n",
      "Collecting guardrails-ai<0.4.0,>=0.3.2 (from llama-index-output-parsers-guardrails)\n",
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      "Using cached guardrails_ai-0.3.3-py3-none-any.whl (148 kB)\n",
      "Installing collected packages: guardrails-ai\n",
      "  Attempting uninstall: guardrails-ai\n",
      "    Found existing installation: guardrails-ai 0.4.2\n",
      "    Uninstalling guardrails-ai-0.4.2:\n",
      "      Successfully uninstalled guardrails-ai-0.4.2\n",
      "Successfully installed guardrails-ai-0.3.3\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install llama-index-llms-openai\n",
    "%pip install llama-index-output-parsers-guardrails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e716f66f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/zaydsimjee/workspace/zayd_forks/llama_index/.venv/lib/python3.11/site-packages (from requests>=2.26.0->tiktoken<0.6.0,>=0.5.1->guardrails-ai) (2.2.1)\n",
      "Requirement already satisfied: setuptools in /Users/zaydsimjee/workspace/zayd_forks/llama_index/.venv/lib/python3.11/site-packages (from zope.interface->eliot<2.0.0,>=1.15.0->guardrails-ai) (65.5.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install guardrails-ai"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "39bc790a",
   "metadata": {},
   "source": [
    "#### Download Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "649bea0c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100 75042  100 75042    0     0   390k      0 --:--:-- --:--:-- --:--:--  396k\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p 'data/paul_graham/'\n",
    "!curl 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' > 'data/paul_graham/paul_graham_essay.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50d3b817-b70e-4667-be4f-d3a0fe4bd119",
   "metadata": {},
   "source": [
    "#### Load documents, build the VectorStoreIndex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "690a6918-7c75-4f95-9ccc-d2c4a1fe00d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import sys\n",
    "\n",
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
    "\n",
    "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03d1691e-544b-454f-825b-5ee12f7faa8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load documents\n",
    "documents = SimpleDirectoryReader(\"./data/paul_graham/\").load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad144ee7-96da-4dd6-be00-fd6cf0c78e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n"
     ]
    }
   ],
   "source": [
    "index = VectorStoreIndex.from_documents(documents, chunk_size=512)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b7d7c61-b5d7-4b8f-b90b-3ebee1103f27",
   "metadata": {},
   "source": [
    "#### Define Query + Guardrails Spec\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fb88295-0840-4e2d-b79b-def0b0a63a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.output_parsers.guardrails import GuardrailsOutputParser"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc25edf7-9343-4e82-a3f1-eec4281a9371",
   "metadata": {},
   "source": [
    "**Define custom QA and Refine Prompts**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba8513e",
   "metadata": {},
   "source": [
    "**Define Guardrails Spec**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4b9201d-fe16-4cc0-8135-a08d9928625d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can either define a RailSpec and initialise a Guard object from_rail_string()\n",
    "# OR define Pydantic classes and initialise a Guard object from_pydantic()\n",
    "# For more info: https://docs.guardrailsai.com/defining_guards/pydantic/\n",
    "# Guardrails recommends Pydantic\n",
    "\n",
    "from pydantic import BaseModel, Field\n",
    "from typing import List\n",
    "import guardrails as gd\n",
    "\n",
    "\n",
    "class BulletPoints(BaseModel):\n",
    "    # In all the fields below, you can define validators as well\n",
    "    # Left out for brevity\n",
    "    explanation: str = Field()\n",
    "    explanation2: str = Field()\n",
    "    explanation3: str = Field()\n",
    "\n",
    "\n",
    "class Explanation(BaseModel):\n",
    "    points: BulletPoints = Field(\n",
    "        description=\"Bullet points regarding events in the author's life.\"\n",
    "    )\n",
    "\n",
    "\n",
    "# Define the prompt\n",
    "prompt = \"\"\"\n",
    "Query string here.\n",
    "\n",
    "${gr.xml_prefix_prompt}\n",
    "\n",
    "${output_schema}\n",
    "\n",
    "${gr.json_suffix_prompt_v2_wo_none}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7af4ebf-1dff-48ec-9fb7-8926af45b6a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from llama_index.llms.openai import OpenAI\n",
    "\n",
    "# Create a guard object\n",
    "guard = gd.Guard.from_pydantic(output_class=Explanation, prompt=prompt)\n",
    "\n",
    "# Create output parse object\n",
    "output_parser = GuardrailsOutputParser(guard)\n",
    "\n",
    "# attach to an llm object\n",
    "llm = OpenAI(output_parser=output_parser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ba18a80-35f4-4fd4-9b13-9f13f84db4fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Context information is below.\n",
      "---------------------\n",
      "{context_str}\n",
      "---------------------\n",
      "Given the context information and not prior knowledge, answer the query.\n",
      "Query: {query_str}\n",
      "Answer: \n",
      "\n",
      "\n",
      "Given below is XML that describes the information to extract from this document and the tags to extract it into.\n",
      "\n",
      "\n",
      "<output>\n",
      "    <object name=\"points\" description=\"Bullet points regarding events in the author's life.\">\n",
      "        <string name=\"explanation\"/>\n",
      "        <string name=\"explanation2\"/>\n",
      "        <string name=\"explanation3\"/>\n",
      "    </object>\n",
      "</output>\n",
      "\n",
      "\n",
      "\n",
      "ONLY return a valid JSON object (no other text is necessary). The JSON MUST conform to the XML format, including any types and format requests e.g. requests for lists, objects and specific types. Be correct and concise.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from llama_index.core.prompts.default_prompts import (\n",
    "    DEFAULT_TEXT_QA_PROMPT_TMPL,\n",
    ")\n",
    "\n",
    "# take a look at the new QA template!\n",
    "fmt_qa_tmpl = output_parser.format(DEFAULT_TEXT_QA_PROMPT_TMPL)\n",
    "print(fmt_qa_tmpl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6caf93b-6345-4c65-a346-a95b0f1746c4",
   "metadata": {},
   "source": [
    "#### Query Index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb9cdf43-0f31-4c36-869b-df9fa50aebdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n",
      "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n",
      "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n"
     ]
    }
   ],
   "source": [
    "query_engine = index.as_query_engine(\n",
    "    llm=llm,\n",
    ")\n",
    "response = query_engine.query(\n",
    "    \"What are the three items the author did growing up?\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc7760b6-5be3-4303-b97e-3f5edacf674b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'points': {'explanation': 'Writing short stories', 'explanation2': 'Programming on the IBM 1401 in 9th grade', 'explanation3': 'Getting a TRS-80 computer and starting programming in 1980'}}\n"
     ]
    }
   ],
   "source": [
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5b06d22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Logs\n",
       "└── ╭────────────────────────────────────────────────── Step 0 ───────────────────────────────────────────────────╮\n",
       "    │ <span style=\"background-color: #f0f8ff\">╭──────────────────────────────────────────────── Prompt ─────────────────────────────────────────────────╮</span> │\n",
       "    │ <span style=\"background-color: #f0f8ff\">│ No prompt                                                                                               │</span> │\n",
       "    │ <span style=\"background-color: #f0f8ff\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span> │\n",
       "    │ <span style=\"background-color: #e7dfeb\">╭──────────────────────────────────────────── Message History ────────────────────────────────────────────╮</span> │\n",
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       "    │ <span style=\"background-color: #e7dfeb\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">╭──────────────────────────────────────────── Raw LLM Output ─────────────────────────────────────────────╮</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│ {                                                                                                       │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│     \"points\": {                                                                                         │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│         \"explanation\": \"Writing short stories\",                                                         │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│         \"explanation2\": \"Programming on the IBM 1401 in 9th grade\",                                     │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│         \"explanation3\": \"Getting a TRS-80 computer and starting programming in 1980\"                    │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│     }                                                                                                   │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">│ }                                                                                                       │</span> │\n",
       "    │ <span style=\"background-color: #f5f5dc\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">╭─────────────────────────────────────────── Validated Output ────────────────────────────────────────────╮</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│ {                                                                                                       │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│     'points': {                                                                                         │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│         'explanation': 'Writing short stories',                                                         │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│         'explanation2': 'Programming on the IBM 1401 in 9th grade',                                     │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│         'explanation3': 'Getting a TRS-80 computer and starting programming in 1980'                    │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│     }                                                                                                   │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">│ }                                                                                                       │</span> │\n",
       "    │ <span style=\"background-color: #f0fff0\">╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯</span> │\n",
       "    ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
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       "    │ \u001b[48;2;240;248;255m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m │\n",
       "    │ \u001b[48;2;231;223;235m╭─\u001b[0m\u001b[48;2;231;223;235m───────────────────────────────────────────\u001b[0m\u001b[48;2;231;223;235m Message History \u001b[0m\u001b[48;2;231;223;235m───────────────────────────────────────────\u001b[0m\u001b[48;2;231;223;235m─╮\u001b[0m │\n",
       "    │ \u001b[48;2;231;223;235m│\u001b[0m\u001b[48;2;231;223;235m \u001b[0m\u001b[48;2;231;223;235mNo message history.\u001b[0m\u001b[48;2;231;223;235m                                                                                    \u001b[0m\u001b[48;2;231;223;235m \u001b[0m\u001b[48;2;231;223;235m│\u001b[0m │\n",
       "    │ \u001b[48;2;231;223;235m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m╭─\u001b[0m\u001b[48;2;245;245;220m───────────────────────────────────────────\u001b[0m\u001b[48;2;245;245;220m Raw LLM Output \u001b[0m\u001b[48;2;245;245;220m────────────────────────────────────────────\u001b[0m\u001b[48;2;245;245;220m─╮\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m{\u001b[0m\u001b[48;2;245;245;220m                                                                                                      \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m    \"points\": {\u001b[0m\u001b[48;2;245;245;220m                                                                                        \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m        \"explanation\": \"Writing short stories\",\u001b[0m\u001b[48;2;245;245;220m                                                        \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m        \"explanation2\": \"Programming on the IBM 1401 in 9th grade\",\u001b[0m\u001b[48;2;245;245;220m                                    \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m        \"explanation3\": \"Getting a TRS-80 computer and starting programming in 1980\"\u001b[0m\u001b[48;2;245;245;220m                   \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m    }\u001b[0m\u001b[48;2;245;245;220m                                                                                                  \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m│\u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m}\u001b[0m\u001b[48;2;245;245;220m                                                                                                      \u001b[0m\u001b[48;2;245;245;220m \u001b[0m\u001b[48;2;245;245;220m│\u001b[0m │\n",
       "    │ \u001b[48;2;245;245;220m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m╭─\u001b[0m\u001b[48;2;240;255;240m──────────────────────────────────────────\u001b[0m\u001b[48;2;240;255;240m Validated Output \u001b[0m\u001b[48;2;240;255;240m───────────────────────────────────────────\u001b[0m\u001b[48;2;240;255;240m─╮\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m{\u001b[0m\u001b[48;2;240;255;240m                                                                                                      \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m    'points': {\u001b[0m\u001b[48;2;240;255;240m                                                                                        \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m        'explanation': 'Writing short stories',\u001b[0m\u001b[48;2;240;255;240m                                                        \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m        'explanation2': 'Programming on the IBM 1401 in 9th grade',\u001b[0m\u001b[48;2;240;255;240m                                    \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m        'explanation3': 'Getting a TRS-80 computer and starting programming in 1980'\u001b[0m\u001b[48;2;240;255;240m                   \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m    }\u001b[0m\u001b[48;2;240;255;240m                                                                                                  \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m│\u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m}\u001b[0m\u001b[48;2;240;255;240m                                                                                                      \u001b[0m\u001b[48;2;240;255;240m \u001b[0m\u001b[48;2;240;255;240m│\u001b[0m │\n",
       "    │ \u001b[48;2;240;255;240m╰─────────────────────────────────────────────────────────────────────────────────────────────────────────╯\u001b[0m │\n",
       "    ╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View a summary of what the guard did\n",
    "guard.history.last.tree"
   ]
  }
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